Acute Risk Monitoring for Oncology Therapy Regimen

NCT07601802 — Completed, 4740 participants, University of California, San Francisco

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Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model

Registry	NCT07601802, acronym ARMOR
Status	Completed
Study type	Observational
Enrollment	4740 (Actual)
Sponsor	University of California, San Francisco (Other); collaborators: Conquer Cancer Foundation, National Cancer Institute (NCI)
Conditions	Cancer, Acute Care Service Utilization
Interventions	Medical record review (Other)
Dates	start 2017-07-01, primary completion 2024-03-31, completion 2024-03-31, first posted 2026-05-22, last updated 2026-05-22
Locations	1 site in United States

-Summary

Patients undergoing outpatient infusion systemic therapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospitalizations. These events impact patient outcomes, treatment decisions, and healthcare costs. To address this need, the Centers for Medicare \& Medicaid Services developed the chemotherapy measure (OP-35). Recent randomized controlled studies indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. This study aims to develop and prospectively validate ML approaches to predict the risk of OP-35 qualifying, potentially preventable, acute care events within 30 days of infusion systemic therapy.

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+Detailed description

OBJECTIVES:

I. Develop and retrospectively validate electronic health record-based machine learning models using routinely collected clinical data from patients receiving systemic therapy to predict risk of potentially preventable OP-35 qualifying acute care events. (Phase 1: Retrospective)

II. Prospectively validate machine learning models across distinct time periods. (Phase 2: Prospective)

III. Understand patterns of care by stratifying and analyzing model performance by treatment type, cancer diagnosis, and race/ethnicity to assess bias and disparities in outcomes.

OUTLINE:

Retrospective and prospective clinical data obtained from medical records will be used to develop and validate predictive machine learning models. Prospective data will be divided into 2 phases: Prospective validation (PV) 1 and PV 2.

+Arms and interventions

Arm	Type	Interventions	Description
Patients receiving cancer therapy at University of California, San Francisco (UCSF)		Other: Medical record review	All adults undergoing systemic cancer-related therapy from July 2017 to March 2024 at any UCSF outpatient, infusion center with available OP-35 data.

Medical record review (Other): Retrospective chart reviews for data collection will be conducted.

+Outcome measures

Primary outcomes

Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model. [Up to 6.75 years]: UCSF patients receiving infusion systemic therapy had clinical data incorporated into machine learning (ML) models to predict risk of Centers for Medicare \& Medicaid Services Chemotherapy Measure (OP-35) qualifying acute care events within 30 days of infusion. Models included variables such as cancer diagnosis, therapeutic agents, and laboratory values. Three ML approaches were employed to train models in predicting OP-35 events. Models were trained and retrospectively validated on data from July 7, 2017, to February 11, 2021, and prospectively validated on 2 cohorts: April 17, 2023, to October 29, 2023 (PV1) and February 19, 2024, to March 31, 2024 (PV2) to generate a validation AUROC. The initial prospective validation occurred over a pre-planned period with the assumption of a 2% event rate, based on the model development data, with an alpha of 0.05 and 84% power to detect an AUROC of 0.75, requiring a sample size of at least 8000 infusions.

+Eligibility

Sex: All, ages: 18 Years to any

Inclusion Criteria:

* Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations.

Exclusion Criteria:

* Patients under the age of 18.
* Patients receiving care as part of a clinical trial.

+Locations (1)

Facility	City	Country	Status
University of California, San Francisco	San Francisco, California	United States

+Investigators

Julian Hong, MD, MS, Principal investigator, University of California, San Francisco

+Funding

NIH grants: R01CA277782